基于运动状态改变的在线全球定位系统轨迹数据压缩

针对基于偏移量计算的轨迹数据压缩算法中对于关键点的评估不足以及基于在线轨迹数据压缩算法中累积误差和对偏移量考虑不足的问题,提出一种基于运动状态改变的在线全球定位系统(GPS)轨迹数据压缩算法——限定同步欧氏距离(SED)的阈值结合算法(SLTA)。该算法通过轨迹点的转向角度大小和速度变化大小来评估轨迹点信息量的大小;同时用SED限制点的偏移量,以达到较好的信息保留度。实验结果表明,SLTA的轨迹压缩率能够达到50%左右,与阈值结合算法(TA)相比,SLTA的平均SED误差(5 m以内)可以忽略不计;相对于基于偏移量计算的轨迹数据压缩算法,SLTA的平均角度误差最小(1.5°~2.3°),运行时间最稳定。SLTA能够稳定有效地进行在线GPS轨迹数据压缩。

Online compression of global positioning system trajectory data based on motion state change

Concerning the insufficient consideration of the cumulative error and offset which online Global Positioning System (GPS) trajectory data compression based on motion state change and the insufficient key point evaluation of online GPS trajectory data compression based on the offset calculation, an online compression of GPS trajectory data based on motion state change, named Synchronous Euclidean Distance (SED) Limited Thresholds Algorithm (SLTA), was proposed. This algorithm used steering angle and speed change to evaluate information of trajectory point. At the same time, SLTA introduced the SED to limit offset of trajectory point. So SLTA could reach better information retention. The experimental results show that the trajectory compression ratio can reach about 50%. Compared with Thresholds Algorithm (TA), the average SED error (less than 5 m) of SLTA can be negligible. For other trajectory data compression algorithms, SLTA's average angel error is the lowest (1.5°-2.3°) and run time is the most stable. SLTA can stably and effectively do online GPS trajectory data compression.